In this paper we propose a specialized LNS approach which utilizes a pre-trained neural construction heuristic. The experiments and challenge results show that the method is viable and, while being in a rather experimental stage, still shows promising performance and can compete (to some extent) with highly optimized algorithms, which in some cases are already in development for several years.
Apart from that, there are still multiple parts of the algorithm which have high potential for improvement in future iterations. The local search can be adapted to be more focused on the specific task and the general interaction with the NCH and the LNS. The destruction heuristics are very simple and currently applied rather naively in a random fashion. Instead, more advanced rules could be used and their selection could be improved with an Adaptive LNS[1]} or they could be replaced completely with a learned operator similar to the repair operator. Finally, the NCH itself can still be improved by explicit learning (or fine-tuning) for the reconstruction task in combination with the destruction operators instead of the current isolated pre-training.
